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High Machine Learning and Application

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Pre Requisites

None required. If you are taking this course as part of the Data Science and Machine Learning Program of Study, Procedural Programming and Data Analytics and Database Design should be taken first. 

Description

Are you ready to elevate your data and database skills and unlock the potential of machine learning? In this dynamic course, you'll dive into the fascinating connection between artificial intelligence and machine learning, uncovering how they shape our world. Discover the crucial role of ethics in AI and machine learning in ensuring responsible design. Harness the power of Python to design and train machine learning models. Explore the intricacies of supervised, unsupervised, and reinforcement learning and the complexities of neural networks. Along the way, you'll also identify the limitations of AI and explore the exciting career opportunities awaiting you in this rapidly evolving field. Get ready to embark on a fun and challenging learning journey!  

This course includes several standards related to the use of artificial intelligence. Students will have the opportunity to utilize generative AI to complete their assessments. 

This is the third course in the Data Science and Machine Learning program of study in the Information Technology career cluster. 

Follow the link below for the Department of Education description for this course: https://www.cpalms.org/PreviewCourseProgram/Preview/4278?frameurl=%2Fcourses%2Fcourse-structure%3Fcoursenumber%3D9007730%26version%3D2024  

Segment One 

  • Intelligent behavior defined 
  • AI applications examples 
  • Use of domain knowledge in AI 
  • Machine learning algorithms 
  • Challenges of AI 
  • Different types of data 
  • Design thinking 
  • AI impact on society 
  • Risks of AI 
  • Bias in AI 
  • AI fairness 

 

Segment Two 

  • Machine learning life cycle 
  • Math in machine learning 
  • Training machine learning models 
  • Evaluating machine learning models 
  • AI hardware 
  • Supervised learning 
  • Unsupervised learning 
  • Neural networks 
  • Reinforcement learning 
  • Working with data 
  • Limitations of machine learning 
  • AI careers 

Besides engaging students in challenging curriculum, the course guides students to reflect on their learning and evaluate their progress through a variety of assessments. Assessments can be in the form of practice lessons, multiple choice questions, writing assignments, projects, research papers, oral assessments, and discussions. This course will use the state-approved grading scale. Each course contains a mandatory final exam or culminating project that will be weighted at 20% of the student’s overall grade.*** 

***Proctored exams can be requested by FLVS at any time and for any reason in an effort to ensure academic integrity. When a proctored exam is administered to assess a student’s integrity, the student must pass the exam with at least a 59.5% to earn credit for the course.